Image Enhancement of 3-D SAR via U-Net Framework
Rong Shen, Shunjun Wei, Zichen Zhou, Jiadian Liang, Xiaoling Zhang, Jun Shi · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Image resolution is the key point for the 3-D synthetic aperture radar (SAR) application, especially in small-scale scene observation. The traditional filter-based image enhancement algorithms used for 3-D SAR may suffer from quality degeneration in case of parameter mismatch. This paper proposes a robust and efficient convolutional neural network (CNN) based U-net framework for 3-D SAR image enhancement. The U-net extracts image features in down sampling and up sampling, which is realized by max pooling and deconvolution layers. We use the mean square error(MSE) as the loss function to estimate the difference between the predicted images and the label, while Adam optimizer updates parameters to achieve the global minimum MSE. Both simulation and measured data verify the effectiveness of the network. The results demonstrate that the U-net outperform some traditional filter-based algorithms.